2
$\begingroup$

Here is a sample output:

anova(fit1,fit2);
Quantile Regression Analysis of Deviance Table

Model: op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9
Joint Test of Equality of Slopes: tau in {  0.15 0.3  }

  Df Resid Df F value Pr(>F)
1  9     1337  0.5256 0.8568

Warning messages:
1: In summary.rq(x, se = "nid", covariance = TRUE) : 93 non-positive fis
2: In summary.rq(x, se = "nid", covariance = TRUE) : 138 non-positive fis

How to interpret the above results?? Does the anova() function give the best model, for tau=0.15 vs. tau=0.3?

$\endgroup$
  • 1
    $\begingroup$ I think it would help to tell people what fit1 and fit2 are $\endgroup$ – Peter Flom Dec 5 '11 at 10:48
1
$\begingroup$

The interpretation of the result of a joint test of equality of slopes is that overall, the effect of the entire set of coefficients is uniform across quantiles.

Understand that this is not a test of the performance of your two models, it simply tests whether slope coefficients of those models, from several quantiles, can be considered not different.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.